Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 70, Issue -, Pages 99-122Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2015.12.013
Keywords
Continuous time Bayesian network; Uncertain evidence; Negative evidence; Exact inference; Importance sampling
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Funding
- NAVY STTR [N10A-009-0292]
- NASA STTR [T13.01-9887]
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The continuous time Bayesian network (CTBN) enables reasoning about complex systems by representing the system as a factored, finite-state, continuous-time Markov process. Inference over the model incorporates evidence, given as state observations through time. The time dimension introduces several new types of evidence that are not found with static models. In this work, we present a comprehensive look at the types of evidence in CTBNs. Moreover, we define and extend inference to reason under uncertainty in the presence of uncertain evidence, as well as negative evidence, concepts extended to static models but not yet introduced into the CTBN model. (c) 2015 Elsevier Inc. All rights reserved.
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